Increasing atmospheric CO2 overrides the historical legacy of multiple stable biome states in Africa
© 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.
| Veröffentlicht in: | The New phytologist. - 1979. - 201(2014), 3 vom: 29. Feb., Seite 908-915 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2014
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| Zugriff auf das übergeordnete Werk: | The New phytologist |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't adaptive dynamic global vegetation model (aDGVM) biome climate change dynamic vegetation model forest multiple stable states savanna Carbon Dioxide |
| Zusammenfassung: | © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust. The dominant vegetation over much of the global land surface is not predetermined by contemporary climate, but also influenced by past environmental conditions. This confounds attempts to predict current and future biome distributions, because even a perfect model would project multiple possible biomes without knowledge of the historical vegetation state. Here we compare the distribution of tree- and grass-dominated biomes across Africa simulated using a dynamic global vegetation model (DGVM). We explicitly evaluate where and under what conditions multiple stable biome states are possible for current and projected future climates. Our simulation results show that multiple stable biomes states are possible for vast areas of tropical and subtropical Africa under current conditions. Widespread loss of the potential for multiple stable biomes states is projected in the 21st Century, driven by increasing atmospheric CO2 . Many sites where currently both tree-dominated and grass-dominated biomes are possible become deterministically tree-dominated. Regions with multiple stable biome states are widespread and require consideration when attempting to predict future vegetation changes. Testing for behaviour characteristic of systems with multiple stable equilibria, such as hysteresis and dependence on historical conditions, and the resulting uncertainty in simulated vegetation, will lead to improved projections of global change impacts |
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| Beschreibung: | Date Completed 01.09.2014 Date Revised 16.04.2021 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1469-8137 |
| DOI: | 10.1111/nph.12551 |